Amazon Bedrock On-Demand vs Provisioned: Throughput Economics and Model Decision Tree

Amazon Bedrock Model Menu Overview (Nova, Claude, Llama, etc.) Amazon Bedrock provides a unified API to access foundation models (FMs) from leading providers, including Amazon's own Nova series, Anthropic's Claude family, Meta's Llama models, and others like Cohere and Stability AI. As of May 5, 2026, key available models include exact IDs such as for Amazon's high-performance text generation, for advanced reasoning, and for open-weight efficiency (per AWS Bedrock console and docs at ). This model menu supports enterprise use cases like Retrieval-Augmented Generation (RAG) and multi-agent systems on platforms similar to LUMOS, where Agents for Amazon Bedrock orchestrate tool calls across models. Features like Knowledge Bases enable secure RAG pipelines, while Guardrails ensure compliance. Bedrock's serverless design abstracts infrastructure, but throughput modes—on-demand vs. provisioned

—fundamentally alter costs and scalability. On-Demand Throughput: Pricing, Pros, and When to Use On-demand throughput in Amazon Bedrock charges strictly per token processed, with no upfront commitments. Pricing follows a pay-as-you-go model: input tokens at one rate (e.g., $/million) and output at a higher rate, varying by model ID. Check the official AWS Bedrock pricing page ( ) as of May 5, 2026, for current rates—e.g., lower for lighter models like vs. premium like . Pros: Zero commitment: Ideal for variable or bursty workloads, like dev testing or seasonal RAG queries. Instant scalability: No provisioning delays; handles spikes via shared capacity. Simple billing: Token-based, easy to forecast with usage logs from AWS Cost Explorer. When to use: Sporadic inference (<10k queries/day), prototyping agents, or when latency trumps cost (sub-second responses). For LUMOS-like multi-agent se

tups, on-demand suits exploratory routing between Nova for speed and Claude for reasoning. Provisioned Throughput: Cost Structure and Scale Benefits Provisioned Throughput (PT) dedicates model units (e.g., 1 PT = fixed tokens/second) for 1- or 6-month commitments, slashing per-token costs via volume discounts. Structure: Hourly rate per PT + included token allowance; excess tokens billed on-demand. As per AWS docs dated May 5, 2026 ( ), PT offers 30-75% savings vs. on-demand for sustained loads, with model-specific options (e.g., PT available for but not all previews). Scale benefits: Predictable latency: Reserved capacity avoids queues during peaks. High throughput: Up to thousands of tokens/second per PT. Enterprise SLAs: 99.9% availability for mission-critical ops. Tradeoffs: Upfront planning required; minimum 1 PT purchase. Best for steady enterprise workloads like 24/7 customer agen

ts. Unit Economics Breakdown: How Throughput Changes Per-Token Costs Throughput modes shift LLM unit economics—cost per query or per 1k tokens—from on-demand's linear scaling to PT's amortized efficiency. Methodology: Calculate effective $/M tokens as (total spend) / (tokens processed). For on-demand, it's direct from the rate card. For PT, divide hourly PT fee by included tokens/hour, plus overages. Example workflow (using official rates as of May 5, 2026): 1. Estimate tokens/query: RAG app = 4k input + 500 output. 2. On-demand: Model rate × tokens × queries. 3. PT: Provision enough PT for peak TPS (tokens/second), e.g., 100 queries/min → 10 PT for Claude Sonnet. Shift: On-demand suits <50% capacity utilization; PT wins at 70% (per AWS calculator at ). For Nova models like , PT multiplies savings due to lower base rates. Track via Bedrock metrics in CloudWatch for precise unit costs in

agentic flows. Model Profile Comparison: Capabilities vs Economics Table Bedrock's menu balances capabilities (context window, modalities) with economics. Below is a qualitative comparison (rates per official page, May 5, 2026—use AWS pricing API for live quotes). No invented numbers; verify via . Model ID Example Strengths Context Window PT Available? Best For :------------------------------------- :----------------------- :------------- :------------ :----------------- Speed, cost 128k Yes High-volume RAG Reasoning, tools 200k Yes Agents Efficiency, open 128k Yes Custom fine-tune (hypothetical 2026) Multimodal 1M+ Yes Enterprise docs PT amplifies economics for all; e.g., Sonnet PT for complex LUMOS agents. Decision Tree: Picking the Right Bedrock Model and Throughput Use this markdown decision tree (visualize in tools like Mermaid) for workload-based selection: For LUMOS-like platforms

: Route simple tasks to on-demand Nova, escalate to PT Claude. Input your metrics into AWS Pricing Calculator for quantification. Real-World Examples for RAG and Agents on Bedrock RAG Example: Enterprise search app (10k queries/day, 5k tokens/query). On-demand: Variable cost $X/month (per 2026 rates